Public record
Software health reportschema 0.34.0 · metrics 2.10.0 · 2026-08-27 21:55 UTC

modelscope / ms-swift

Use PEFT or Full-parameter to CPT/SFT/DPO/GRPO 600+ LLMs (Qwen3.6, DeepSeek-V4, GLM-5.1, InternLM3, Llama4, ...) and 300+ MLLMs (Qwen3-VL, Qwen3-Omni, InternVL3.5, Ovis2.5, GLM4.5v, Gemma4, Llava, Phi4, ...) (AAAI 2025).

PythonApache-2.0★ 15,386 stars⑂ 1,636 forkssince Aug 2023View on GitHub ↗
KindCommand-line toolLibraryhow this is determined

modelscope/ms-swift holds a health index of 94 out of 100, placing it in the Exceptional band. It scores highest on Vitality (100/100) and lowest on Security (44/100). It was last updated today. A single contributor accounts for most of its recent work.

94
overall / 100
Exceptional

Software health index

Metrics are grouped into weighted categories on one standardized 1–100 scale. Overall starts as their weighted mean, calibrated against the distribution of the public record so bands carry percentile meaning; when public evidence triggers the High-Risk Jurisdiction Policy, the rating is adjusted and receives an At Risk ceiling of 34.

94
Exceptional93-100The record's top tier (≈ top 5%); essentially all checked criteria met
Excellent80-92Strong across the board; minor gaps
Good65-79Healthy; gaps are limited and manageable
Moderate50-64Acceptable with notable gaps; review recommended
Weak35-49Material weaknesses across several areas
At Risk20-34Significant weaknesses; adoption warrants caution
Critical1-19Severe problems (abandoned, single-maintainer, no hygiene)
VitalityCommunity &AdoptionSustainability &GovernanceEngineeringQualitySecurityAI Readiness

Score profile

Each axis is a category. The shape matters more than the average — a healthy subject fills the whole shape, while a spike-and-crater profile means strength in one dimension is masking risk in another.

The weighted overall 81 is calibrated to 94 on the published index scale (record calibration 2026-08-02).

Ownership

ModelScopeOrganization
6,540 followers52 public repossince Jul 2022

This repository is backed by an organization — shared, accountable stewardship that can outlive any single maintainer.

Package ecosystems

RegistryPackageVersionDownloads / moVersionsLast publishTags
PyPIms_swift4.5.2110,96513511 days agotransformersllmloramegatrongrposft

Metrics by category

Vitality

Is the project alive — is code being written and are releases shipping?

100Exceptional · 21% of overall

Development activity

100Exceptional
How it's scored
36/36Push recencylast push 0 days ago
36/36Commit cadence52/52 weeks with commits
18/18Commit volume1,282 commits in the last year
10/10OpenSSF Scorecard: Maintained30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
Inputs used
commits_last_year1,282
human_commit_share1
days_since_last_push0
active_weeks_last_year52

Release discipline

100Exceptional
How it's scored
27/27Ships releases100 releases published
36/36Release recencylatest release 11 days ago
27/27Release cadencea release every ~8.5 days
0/10OpenSSF Scorecard: Signed-Releasesno data
Inputs used
releases_count100
latest_release_tagv4.5.2
releases_from_tagsno
days_since_latest_release11
mean_days_between_releases8.5
Excluded from scoring (no data or not applicable): OpenSSF Scorecard: Signed-Releases. Remaining weights renormalized.

Community & Adoption

Does the project have users, downloads, attention, and a welcoming setup for contributors?

91Excellent · 17% of overall
How it's scored
60/60Stars15,386 stars
25/25Forks1,636 forks
9.4/15Watchers51 watchers
Inputs used
forks1,636
stars15,386
watchers51
growth_stateunverified
growth_factor_pct100
growth_unverified_reasonno_history

Community health

92Excellent
How it's scored
22.5/22.5README
22.5/22.5Licenserecognized license (Apache-2.0)
18/18CONTRIBUTING guide
13.5/13.5Code of conduct
0/7.2Issue template
6.3/6.3PR template
Inputs used
has_readmeyes
has_licenseyes
readme_badges6
has_contributingyes
has_issue_templateno
has_code_of_conductyes
readme_badge_servicesbadge.fury.io, shields.io
has_pull_request_templateyes
How it's scored
67.3/80Monthly downloads110,965 downloads/month across pypi
0/20Registry dependentsnot reported by this ecosystem
Inputs used
packagesms_swift
dependents
ecosystemspypi
total_downloads
monthly_downloads110,965
unverified_packages_excluded
Excluded from scoring (no data or not applicable): Registry dependents. Remaining weights renormalized.

Sustainability & Governance

Will the project survive its people — bus factor, responsiveness, who backs it, and package upkeep?

74Good · 23% of overall
How it's scored
9/54Bus factor1 contributor(s) cover half of all commits
9.8/22.5Commit distributiontop contributor authored 56% of commits
13.5/13.5Contributor breadth100 contributors
10/10OpenSSF Scorecard: Contributorsproject has 9 contributing companies or organizations
Inputs used
bus_factor1
contributors_sampled100
top_contributor_share0.563
How it's scored
37.6/42Issue resolution90% of issues closed
27.6/30PR acceptance3,523/3,825 decided PRs merged
10.7/13Newcomer PR acceptance14/17 first-time contributors' PRs merged in 30d
13.5/15OpenSSF Scorecard: Code-ReviewFound 28/30 approved changesets -- score normalized to 9
Inputs used
merged_prs3,523
open_issues537
closed_issues4,592
prs_merged_7d36
prs_decided_7d36
prs_merged_30d47
prs_decided_30d60
issue_closed_ratio0.895
closed_unmerged_prs302
first_time_authors_30d11
first_time_prs_merged_30d14
first_time_prs_decided_30d17
How it's scored
30/30Ownership backingorganization-owned
0/20Verified domain
25/25Owner reach6,540 followers of modelscope
20.7/25Track record52 public repos, account ~4 yr old
Inputs used
followers6,540
owner_typeOrganization
is_verifiedno
owner_loginmodelscope
public_repos52
account_age_days1,494

Package maintenance

100Exceptional
How it's scored
25/25Published & resolvable1 package(s) on pypi
35/35Publish recencylatest publish 11 days ago
20/20Version history135 published versions
20/20Not deprecatedactive, not deprecated or yanked
Inputs used
packagesms_swift
ecosystemspypi
any_deprecatedno
min_days_since_publish11

Engineering Quality

Are baseline engineering and documentation practices in place?

96Exceptional · 19% of overall
How it's scored
24/24CI workflows6 workflow(s)
24/24Tests present
16/16Linter configsetup.cfg ([flake8], [isort], [yapf])
9.6/9.6Pre-commit hooks
0/6.4.editorconfig
20/20OpenSSF Scorecard: CI-Tests29 out of 29 merged PRs checked by a CI test -- score normalized to 10
Inputs used
has_ciyes
has_testsyes
has_editorconfigno
has_linter_configyes
has_precommit_configyes

Documentation

100Exceptional
How it's scored
30/30README
25/25Documentation directory
15/15Documentation / homepage sitehttps://swift.readthedocs.io/zh-cn/latest/
10/10Repository description
10/10Topics20 topics
10/10Wiki
Inputs used
topicsllm, lora, llama, sft, multimodal, peft, internvl, liger, deepseek-r1, embedding, grpo, open-r1, megatron, llama4, qwen3, reranker, moe, qwen3-vl, qwen3-omni, qwen3-6
has_wikiyes
homepagehttps://swift.readthedocs.io/zh-cn/latest/
docs_sitehttps://swift.readthedocs.io/zh-cn/latest/
has_readmeyes
has_docs_diryes
has_descriptionyes

Security

Are visible security and supply-chain practices strong, without unresolved high-risk jurisdiction exposure?

44Weak · 16% of overall
How it's scored
7.5/7.5Binary-Artifactsno binaries found in the repo
0/7.5Branch-Protectionno data
2.5/2.5CI-Tests29 out of 29 merged PRs checked by a CI test -- score normalized to 10
0/2.5CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
6.8/7.5Code-ReviewFound 28/30 approved changesets -- score normalized to 9
2.5/2.5Contributorsproject has 9 contributing companies or organizations
0/10Dangerous-Workflowdangerous workflow patterns detected
0/7.5Dependency-Update-Toolno update tool detected
0/5Fuzzingproject is not fuzzed
2.5/2.5Licenselicense file detected
7.5/7.5Maintained30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
0/5Packagingno data
0/5Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0/5SASTSAST tool is not run on all commits -- score normalized to 0
1.5/5Security-Policysecurity policy file detected
0/7.5Signed-Releasesno data
0/7.5Token-Permissionsdetected GitHub workflow tokens with excessive permissions
6.8/7.5Vulnerabilities1 existing vulnerabilities detected
Inputs used
sourceopenssf_scorecard
checks_evaluated15
scorecard_versionv5.5.0
checks_inconclusive3
scorecard_aggregate4.4
Excluded from scoring (no data or not applicable): Branch-Protection, Packaging, Signed-Releases. Remaining weights renormalized.
How it's scored
7.4/35Direct dependencies free of known advisories3 affected: pillow 11.3.0 (critical 9.1), gradio 5.50.0 (high 8.6), datasets 4.8.4 (unknown)
10/25Indirect dependencies free of known advisories1 affected: starlette 0.52.1 (high 7.5)
24.5/40No advisories left outstanding3 advisory-carrying package(s) unaddressed past 90 days; oldest published 197 days ago
Inputs used
sourceosv
advisories60
affected_packages4
assessed_packages129
unassessed_packages0
affected_by_severitycritical 1, high 2, unknown 1
direct_affected_packages3
Matched the pypi:ms_swift@4.5.2 runtime dependency closure — what installing the published package pulls in — 129 packages. Reachability is not analyzed.

AI Readiness

How well is the repo equipped to be developed and maintained with AI coding agents? Carries a deliberately small weight (4%): agent tooling is a real maintenance signal, but a repository with none can still reach 100/100.

57Moderate · 4% of overall
How it's scored
0/45Agent instructionsno CLAUDE.md / AGENTS.md / editor rules
0/15Machine-readable docs (llms.txt)
40/40Legible commit history94 of 100 human commits state their intent (structured subject or explanatory body)
Inputs used
has_llms_txtno
llms_txt_url
legible_history_share0.94
agent_instruction_files
agent_instruction_max_bytes
How it's scored
18/18One-command bootstrapMakefile, docs/Makefile
22/22Automated tests
11/11Lint / format configsetup.cfg ([flake8], [isort], [yapf])
0/11Static type checking
0/10Reproducible environment
4/10Demonstrated agent practice2 of the last 100 commits agent-authored or agent-credited
0/8Automated maintenanceno automated dependency updates observed
0/10OpenSSF Scorecard: Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
Inputs used
has_nixno
has_testsyes
lockfiles
has_dockerfileno
typed_languageno
bootstrap_filesMakefile, docs/Makefile
has_devcontainerno
has_linter_configyes
typecheck_configs
agent_commit_share0.02
toolchain_manifests
dependency_bot_commit_share0
How it's scored
0/45Type-checkable codePython without a type-check config
54.3/55Manageable file sizes9/706 source files over 60KB
Inputs used
primary_languagePython
largest_source_bytes136,370
source_files_sampled706
oversized_source_files9
How it's scored
0/40API schema (OpenAPI/GraphQL/proto)not applicable to this kind of software
0/20MCP servernot applicable to this kind of software
40/40Runnable examplesexamples, notebooks, sample
Inputs used
example_dirsexamples, notebooks, sample
has_mcp_signalno
api_schema_files
interfaces_expected_of
Excluded from scoring (no data or not applicable): API schema (OpenAPI/GraphQL/proto), MCP server. Remaining weights renormalized.

Key facts

15,386GitHub stars
100contributors
1,282commits, last 12 months
0days since last push
100releases
1bus factor
537open issues
PyPIpackage ecosystems

Data collection warnings

  • Star history unavailable: GitHub GraphQL error: Resource not accessible by personal access token
  • First-time contributor figures cover 12 of 28 authors (cap 12)

More detail

Star and fork history 0 ★ / 1,636 ⇿
0Stars
1,636Forks
58Releases

When each star and fork was added, collected from GitHub and bucketed by day. Cumulative growth sits directly above the daily additions it is made of, so the two read against each other: steady organic accretion looks nothing like an abrupt, short-lived burst. Where that difference is measurable, it is reported as growth authenticity.

Only the most recent history is shown — this repository exceeds the collection window, so the earliest history is not captured.

5007501,0001,2501,5001,7501,636132025-062026-012026-08
Major 1Minor 13Patch 44

Each point covers 2 days.

OpenSSF Scorecard 4.4 / 10
4.4aggregate

Independent, tool-agnostic security assessment from the open-source OpenSSF Scorecard. Each check rewards a security practice, not a specific vendor's tool. Checks Scorecard could not determine are marked n/a and excluded from the security score (never counted as zero).Scorecard v5.5.0 · 2026-08-27 21:54 UTC

10Binary-Artifactsno binaries found in the repo
n/aBranch-Protectioninternal error: error during branchesHandler.setup: internal error: some github tokens can't read classic branch protection rules: https://github.com/ossf/scorecard-action/blob/main/docs/authentication/fine-grained-auth-token.md
10CI-Tests29 out of 29 merged PRs checked by a CI test -- score normalized to 10
0CII-Best-Practicesno effort to earn an OpenSSF best practices badge detected
9Code-ReviewFound 28/30 approved changesets -- score normalized to 9
10Contributorsproject has 9 contributing companies or organizations
0Dangerous-Workflowdangerous workflow patterns detected
0Dependency-Update-Toolno update tool detected
0Fuzzingproject is not fuzzed
10Licenselicense file detected
10Maintained30 commit(s) and 11 issue activity found in the last 90 days -- score normalized to 10
n/aPackagingpackaging workflow not detected
0Pinned-Dependenciesdependency not pinned by hash detected -- score normalized to 0
0SASTSAST tool is not run on all commits -- score normalized to 0
3Security-Policysecurity policy file detected
n/aSigned-Releasesno releases found
0Token-Permissionsdetected GitHub workflow tokens with excessive permissions
9Vulnerabilities1 existing vulnerabilities detected
All dependencies 62

Full resolved dependency set from the GitHub dependency graph: 0 direct and 62 indirect (transitive) packages. The transitive closure is complete when the repository commits a lockfile.

RegistryPackageVersionRelation
PyPIaccelerateindirect
PyPIaddictindirect
PyPIaiohttpindirect
PyPIattrdictindirect
PyPIbinpackingindirect
PyPIcharset-normalizerindirect
PyPIcpm-kernelsindirect
PyPIdaciteindirect
PyPIdatasetsindirect
PyPIdecoratorindirect
PyPIdocutilsindirect
PyPIeinopsindirect
PyPIevalscopeindirect
PyPIexpecttestindirect
PyPIfastapiindirect
PyPIflake8indirect
PyPIgradioindirect
PyPIimportlib-metadataindirect
PyPIisortindirect
PyPIjson-repairindirect
PyPImatplotlibindirect
PyPImcore-bridgeindirect
PyPImegatron-coreindirect
PyPImodelscopeindirect
PyPImyst-parserindirect
PyPInltkindirect
PyPInumpyindirect
PyPIopenaiindirect
PyPIoss2indirect
PyPIpandasindirect
PyPIpeftindirect
PyPIpillowindirect
PyPIpre-commitindirect
PyPIpytestindirect
PyPIpyyamlindirect
PyPIrayindirect
PyPIrecommonmarkindirect
PyPIrequestsindirect
PyPIrougeindirect
PyPIsafetensorsindirect
PyPIscipyindirect
PyPIsentencepieceindirect
PyPIsimplejsonindirect
PyPIsortedcontainersindirect
PyPIsphinxindirect
PyPIsphinx-book-themeindirect
PyPIsphinx-copybuttonindirect
PyPIsphinx-markdown-tablesindirect
PyPIsphinx-rtd-themeindirect
PyPIsphinxcontrib-mermaidindirect
PyPIswanlabindirect
PyPItensorboardindirect
PyPItiktokenindirect
PyPItorchaudio2.7.1indirect
PyPItorchvision0.22.1indirect
PyPItqdmindirect
PyPItransformersindirect
PyPItransformers-stream-generatorindirect
PyPItrlindirect
PyPIuvicornindirect
PyPIyapf0.30.0indirect
PyPIzstandardindirect
Dependency advisories 4

Installing pypi:ms_swift@4.5.2 pulls in 129 packages, direct and transitive: 4 carry known advisories, of which 3 are direct dependencies.

PackageVersionRelationSeverityAdvisoriesFixed in
pillow11.3.0directcritical3612.3.0
gradio5.50.0directhigh136.16.0
starlette0.52.1indirecthigh101.3.1
datasets4.8.4directunknown15.0.1

An advisory means the version recorded in the dependency graph falls inside an advisory’s affected range. Reachability is not analysed, and the graph includes development and test pins — a finding may concern tooling rather than shipped software.

Raw JSON report machine-readable

Feedback

Spotted something off in this report, or have thoughts to share? Wrong measurements, missed tooling, ideas, questions — anything is welcome. Every message is read and gets a response.

The message is kept through sign-in.

Scores are signals, not warranties. They reflect publicly visible practices on GitHub — not a code audit, and not a security guarantee.

Missing data is excluded and weights renormalized, never scored as zero. Methodology is versioned and open: metrics v2.10.0, schema v0.34.0 — full methodology · metrics wiki.

How one result sits in the wider record: aggregate statisticsPyPI.